PrePostNEGD.plot#

PrePostNEGD.plot(*, round_to=None, ci_prob=0.94, hdi_prob=None, kind='ribbon', ci_kind='hdi', num_samples=50, figsize=(7, 9), show=True, legend_kwargs=None)[source]#

Plot the pre-post non-equivalent group design results.

Parameters:

  • round_to (int | None) – Number of decimals used to round numerical results in the figure. Defaults to None, in which case 2 significant figures are used.

  • ci_prob (float) – Probability mass of the highest density interval drawn around the posterior predictive bands for the control and treatment groups, and around the posterior of the estimated treatment effect. Must be in (0, 1]. Defaults to HDI_PROB (currently 0.94).

  • hdi_prob (float | None) – Deprecated. Use ci_prob instead.

  • kind (Literal['ribbon', 'histogram', 'spaghetti']) – How posterior uncertainty is rendered via plot_xY(). Defaults to "ribbon". For "spaghetti" and "histogram", the legend shows individual sample lines rather than a shaded band.

  • ci_kind (Literal['hdi', 'eti']) – Credible interval type when kind="ribbon". Defaults to "hdi".

  • num_samples (int) – Number of posterior draws when kind="spaghetti". Defaults to 50. Ignored for other kinds.

  • figsize (tuple[float, float]) – Width and height of the figure in inches, passed to matplotlib.pyplot.subplots(). Defaults to (7, 9).

  • show (bool) – Whether to automatically display the plot. Defaults to True.

  • legend_kwargs (dict[str, Any] | None) – Keyword arguments to adjust legend placement and styling. Supported keys: loc, bbox_to_anchor, fontsize, frameon, title (bbox_transform is accepted alongside bbox_to_anchor). The existing legend is modified in place so that custom handles are preserved.

Returns:

  • fig (matplotlib.figure.Figure) – The figure that was created.

  • ax (list[matplotlib.axes.Axes]) – The two axes (top: scatter and posterior predictive bands, bottom: estimated treatment effect posterior).

Return type:

tuple[Figure, list[Axes]]